{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 量子神经网络的贫瘠高原效应\n",
    "\n",
    "\n",
    "<em> Copyright (c) 2021 Institute for Quantum Computing, Baidu Inc. All Rights Reserved. </em>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 概览\n",
    "\n",
    "在经典神经网络的训练中，基于梯度的优化方法不仅仅会遇到局部最小值的问题，同时还要面对鞍点等附近梯度近似于零的几何结构。相对应的，在量子神经网络中也存在着一种**贫瘠高原效应**（barren plateaus）。这个奇特的现象首先是由 McClean et al. 在 2018 年发现 [[arXiv:1803.11173]](https://arxiv.org/abs/1803.11173)。简单来说，就是当你选取的随机电路结构满足一定复杂程度时优化曲面（optimization landscape）会变得很平坦，从而导致基于梯度下降的优化方法很难找到全局最小值。对于大多数的变分量子算法（VQE等等），这个现象意味着当量子比特数量越来越多时，选取随机结构的电路有可能效果并不好。这会让你设计的损失函数所对应的优化曲面变成一个巨大的高原，让从而导致量子神经网络的训练愈加困难。你随机找到的初始值很难逃离这个高原，梯度下降收敛速度会很缓慢。\n",
    "\n",
    "![BP-fig-barren](./figures/BP-fig-barren-cn.png)\n",
    "\n",
    "图片由 [Gradient Descent Viz](https://github.com/lilipads/gradient_descent_viz) 生成\n",
    "\n",
    "本教程主要讨论如何在量桨（Paddle Quantum）平台上展示贫瘠高原这一现象。其中并不涉及任何算法创新，但能提升读者对于量子神经网络训练中梯度问题的认识。首先我们先引入必要的 library和 package：\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-02T12:20:39.463025Z",
     "start_time": "2021-03-02T12:20:36.336398Z"
    }
   },
   "outputs": [],
   "source": [
    "import time\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt \n",
    "import paddle\n",
    "from paddle import matmul\n",
    "from paddle_quantum.circuit import UAnsatz\n",
    "from paddle_quantum.utils import dagger\n",
    "from paddle_quantum.state import density_op"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 随机的网络结构\n",
    "\n",
    "这里我们按照原作者 McClean (2018)论文中提及的类似方法搭建如下随机电路（目前我们不支持内置的 control-Z 门，方便起见用 CNOT 替代）：\n",
    "\n",
    "![BP-fig-Barren_circuit](./figures/BP-fig-Barren_circuit.png)\n",
    "\n",
    "首先作用在所有量子比特上绕布洛赫球的 Y-轴旋转 $\\pi/4$。\n",
    "\n",
    "其余的结构加起来构成一个模块（Block）, 每个模块共分为两层：\n",
    "\n",
    "- 第一层搭建随机的旋转门, 其中 $R_{\\ell,n} \\in \\{R_x, R_y, R_z\\}$。下标 $\\ell$ 表示处于第 $\\ell$ 个重复的模块， 上图中 $\\ell =1$。第二个下标 $n$ 表示作用在第几个量子比特上。\n",
    "- 第二层由 CNOT 门组成，作用在每两个相邻的量子比特上。\n",
    "\n",
    "在量桨中, 我们可以这么搭建。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-02T12:20:39.972053Z",
     "start_time": "2021-03-02T12:20:39.962259Z"
    }
   },
   "outputs": [],
   "source": [
    "def rand_circuit(theta, target, num_qubits):\n",
    "    # 我们需要将 Numpy array 转换成 Paddle 中的 Tensor\n",
    "    const = paddle.to_tensor(np.array([np.pi/4]))\n",
    "    \n",
    "    # 初始化量子电路\n",
    "    cir = UAnsatz(num_qubits)\n",
    "    \n",
    "    # ============== 第一层 ==============\n",
    "    # 固定角度的 Ry 旋转门\n",
    "    for i in range(num_qubits):\n",
    "        cir.ry(const, i)\n",
    "\n",
    "    # ============== 第二层 ==============\n",
    "    # target是一个随机的数组,用来帮助我们抽取随机的单比特门 \n",
    "    for i in range(num_qubits):\n",
    "        if target[i] == 0:\n",
    "            cir.rz(theta[i], i)\n",
    "        elif target[i] == 1:\n",
    "            cir.ry(theta[i], i)\n",
    "        else:\n",
    "            cir.rx(theta[i], i)\n",
    "            \n",
    "    # ============== 第三层 ==============\n",
    "    # 搭建两两相邻的 CNOT 门\n",
    "    for i in range(num_qubits - 1):\n",
    "        cir.cnot([i, i + 1])\n",
    "        \n",
    "    return cir.U"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 损失函数与优化曲面 \n",
    "\n",
    "当我们确定了电路的结构之后，我们还需要定义一个损失函数（loss function）来确定优化曲面。按照原作者 McClean (2018)论文中提及的，我们采用 VQE算法中常用的损失函数：\n",
    "\n",
    "$$\n",
    "\\mathcal{L}(\\boldsymbol{\\theta})= \\langle0| U^{\\dagger}(\\boldsymbol{\\theta})H U(\\boldsymbol{\\theta}) |0\\rangle,\n",
    "\\tag{1}\n",
    "$$\n",
    "\n",
    "其中的酉矩阵 $U(\\boldsymbol{\\theta})$ 就是我们上一部分搭建的带有随机结构的量子神经网络。对于其中的哈密顿量 $H$ 我们不妨先取最简单的形式 $H = |00\\cdots 0\\rangle\\langle00\\cdots 0|$。设定好这些后，我们就可以从最简单的两个量子比特的情形开始采样了 -- 生成300组随机网络结构和不同的随机初始参数 $\\{\\theta_{\\ell,n}^{(i)}\\}_{i=1}^{300}$。每次计算梯度都是按照 VQE 的解析梯度公式计算关于 **第一个参数 $\\theta_{1,1}$** 的偏导数。然后统计得到的这300个梯度的平均值和方差。其中解析梯度的公式为：\n",
    "\n",
    "$$\n",
    "\\partial \\theta_{j} \n",
    "\\equiv \\frac{\\partial \\mathcal{L}}{\\partial \\theta_j}\n",
    "= \\frac{1}{2} \\big[\\mathcal{L}(\\theta_j + \\frac{\\pi}{2}) - \\mathcal{L}(\\theta_j - \\frac{\\pi}{2})\\big].\n",
    "\\tag{2}\n",
    "$$\n",
    "\n",
    "具体推导请参见：[arXiv:1803.00745](https://arxiv.org/abs/1803.00745)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-02T12:20:52.236108Z",
     "start_time": "2021-03-02T12:20:40.850822Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "主程序段总共运行了 4.645057678222656 秒\n",
      "采样 300 个随机网络关于第一个参数梯度的均值是： 0.005925709445960606\n",
      "采样 300 个随机网络关于第一个参数梯度的方差是： 0.028249053148446363\n"
     ]
    }
   ],
   "source": [
    "# 超参数设置\n",
    "np.random.seed(42)        # 固定 Numpy 的随机种子\n",
    "N = 2                     # 设置量子比特数量 \n",
    "samples = 300             # 设定采样随机网络结构的数量\n",
    "THETA_SIZE = N            # 设置参数 theta 的大小\n",
    "ITR = 1                   # 设置迭代次数\n",
    "LR = 0.2                  # 设定学习速率\n",
    "SEED = 1                  # 固定优化器中随机初始化的种子\n",
    "\n",
    "# 初始化梯度数值的寄存器\n",
    "grad_info = []\n",
    "\n",
    "paddle.seed(SEED)\n",
    "\n",
    "class manual_gradient(paddle.nn.Layer):\n",
    "    \n",
    "    # 初始化一个可学习参数列表，并用 [0, 2*pi] 的均匀分布来填充初始值\n",
    "    def __init__(self, shape, param_attr=paddle.nn.initializer.Uniform(low=0.0, high=2 * np.pi),dtype='float64'):\n",
    "        super(manual_gradient, self).__init__()\n",
    "        \n",
    "        # 将 Numpy array 转换成 Paddle 中的 Tensor\n",
    "        self.H = paddle.to_tensor(density_op(N))\n",
    "        \n",
    "    # 定义损失函数和前向传播机制    \n",
    "    def forward(self):\n",
    "        \n",
    "        # 初始化三个 theta 参数列表\n",
    "        theta_np = np.random.uniform(low=0., high= 2 * np.pi, size=(THETA_SIZE))\n",
    "        theta_plus_np = np.copy(theta_np) \n",
    "        theta_minus_np = np.copy(theta_np) \n",
    "        \n",
    "        # 修改用以计算解析梯度\n",
    "        theta_plus_np[0] += np.pi/2\n",
    "        theta_minus_np[0] -= np.pi/2\n",
    "        \n",
    "        # 将 Numpy array 转换成 Paddle 中的 Tensor\n",
    "        theta = paddle.to_tensor(theta_np)\n",
    "        theta_plus = paddle.to_tensor(theta_plus_np)\n",
    "        theta_minus = paddle.to_tensor(theta_minus_np)\n",
    "        \n",
    "        # 生成随机目标，在 rand_circuit 中随机选取电路门\n",
    "        target = np.random.choice(3, N)      \n",
    "        \n",
    "        U = rand_circuit(theta, target, N)\n",
    "        U_dagger = dagger(U) \n",
    "        U_plus = rand_circuit(theta_plus, target, N)\n",
    "        U_plus_dagger = dagger(U_plus) \n",
    "        U_minus = rand_circuit(theta_minus, target, N)\n",
    "        U_minus_dagger = dagger(U_minus) \n",
    "\n",
    "        # 计算解析梯度\n",
    "        grad = (paddle.real(matmul(matmul(U_plus_dagger, self.H), U_plus))[0][0]  \n",
    "                - paddle.real(matmul(matmul(U_minus_dagger, self.H), U_minus))[0][0])/2  \n",
    "        \n",
    "        return grad\n",
    "\n",
    "# 定义主程序段\n",
    "def main():\n",
    "    \n",
    "    # 设置QNN的维度\n",
    "    sampling = manual_gradient(shape=[THETA_SIZE])\n",
    "\n",
    "    # 采样获得梯度信息\n",
    "    grad = sampling()   \n",
    "        \n",
    "    return grad.numpy()\n",
    "\n",
    "\n",
    "# 记录运行时间\n",
    "time_start = time.time()\n",
    "\n",
    "# 开始采样\n",
    "for i in range(samples):\n",
    "    if __name__ == '__main__':\n",
    "        grad = main()\n",
    "        grad_info.append(grad)\n",
    "\n",
    "time_span = time.time() - time_start        \n",
    "print('主程序段总共运行了', time_span, '秒')\n",
    "print(\"采样\", samples, \"个随机网络关于第一个参数梯度的均值是：\", np.mean(grad_info))\n",
    "print(\"采样\", samples, \"个随机网络关于第一个参数梯度的方差是：\", np.var(grad_info))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 优化曲面的可视化\n",
    "\n",
    "接下来我们试着利用 Matplotlib来可视化这个优化曲面（optimization landscape）。在**两个量子比特**的情况下，我们只有两个参数 $\\theta_1$ 和 $\\theta_2$, 并且第二层的随机电路电路总共有9种情况。这个很容易画出来：\n",
    "\n",
    "![BP-fig-landscape2](./figures/BP-fig-landscape2.png)\n",
    "\n",
    "可以看到的是最后一张图中 $R_z$-$R_z$ 结构所展示出的平原结构是我们非常不想见到的。在这种情况下，我们不能收敛到理论最小值。如果你想自己试着画一些优化曲面，不妨参见以下代码："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-02T12:21:49.972769Z",
     "start_time": "2021-03-02T12:21:45.792119Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "主程序段总共运行了 1.504277229309082 秒\n"
     ]
    }
   ],
   "source": [
    "# 引入必要的 package\n",
    "from matplotlib import cm\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from matplotlib.ticker import LinearLocator, FormatStrFormatter\n",
    "\n",
    "time_start = time.time()\n",
    "N = 2\n",
    "\n",
    "# 设置图像比例 竖：横 = 0.3 \n",
    "fig = plt.figure(figsize=plt.figaspect(0.3))\n",
    "\n",
    "# 生成 x, y 轴上的点\n",
    "X = np.linspace(0, 2 * np.pi, 80)\n",
    "Y = np.linspace(0, 2 * np.pi, 80)\n",
    "\n",
    "# 生成 2D 网格 (mesh)\n",
    "xx, yy = np.meshgrid(X, Y)\n",
    "\n",
    "\n",
    "# 定义必要的逻辑门\n",
    "def rx(theta):\n",
    "    mat = np.array([[np.cos(theta/2), -1j * np.sin(theta/2)],\n",
    "                    [-1j * np.sin(theta/2), np.cos(theta/2)]])\n",
    "    return mat\n",
    "\n",
    "def ry(theta):\n",
    "    mat = np.array([[np.cos(theta/2), -1 * np.sin(theta/2)],\n",
    "                    [np.sin(theta/2), np.cos(theta/2)]])\n",
    "    return mat\n",
    "\n",
    "def rz(theta):\n",
    "    mat = np.array([[np.exp(-1j * theta/2), 0],\n",
    "                    [0, np.exp(1j * theta/2)]])\n",
    "    return mat\n",
    "\n",
    "def CNOT():\n",
    "    mat = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 0, 1], [0, 0, 1, 0]])\n",
    "    return mat\n",
    "\n",
    "# ============= 第一张图 =============\n",
    "# 我们可视化第二层是 kron(Ry, Ry) 的情况\n",
    "ax = fig.add_subplot(1, 2, 1, projection='3d')\n",
    "\n",
    "# 向前传播计算损失函数：\n",
    "def cost_yy(para):\n",
    "    L1 = np.kron(ry(np.pi/4), ry(np.pi/4))\n",
    "    L2 = np.kron(ry(para[0]), ry(para[1]))\n",
    "    U = np.matmul(np.matmul(L1, L2), CNOT())\n",
    "    H = np.zeros((2 ** N, 2 ** N))\n",
    "    H[0, 0] = 1\n",
    "    val = (U.conj().T @ H@ U).real[0][0]\n",
    "    return val\n",
    "\n",
    "# 画出图像\n",
    "Z = np.array([[cost_yy([x, y]) for x in X] for y in Y]).reshape(len(Y), len(X))\n",
    "surf = ax.plot_surface(xx, yy, Z, cmap='plasma')\n",
    "ax.set_xlabel(r\"$\\theta_1$\")\n",
    "ax.set_ylabel(r\"$\\theta_2$\")\n",
    "ax.set_title(\"Optimization Landscape for Ry-Ry Layer\")\n",
    "\n",
    "# ============= 第二张图 =============\n",
    "# 我们可视化第二层是 kron(Rx, Rz) 的情况\n",
    "ax = fig.add_subplot(1, 2, 2, projection='3d')\n",
    "\n",
    "\n",
    "def cost_xz(para):\n",
    "    L1 = np.kron(ry(np.pi/4), ry(np.pi/4))\n",
    "    L2 = np.kron(rx(para[0]), rz(para[1]))\n",
    "    U = np.matmul(np.matmul(L1, L2), CNOT())\n",
    "    H = np.zeros((2 ** N, 2 ** N))\n",
    "    H[0, 0] = 1\n",
    "    val = (U.conj().T @ H @ U).real[0][0]\n",
    "    return val\n",
    "\n",
    "Z = np.array([[cost_xz([x, y]) for x in X] for y in Y]).reshape(len(Y), len(X))\n",
    "surf = ax.plot_surface(xx, yy, Z, cmap='viridis')\n",
    "ax.set_xlabel(r\"$\\theta_1$\")\n",
    "ax.set_ylabel(r\"$\\theta_2$\")\n",
    "ax.set_title(\"Optimization Landscape for Rx-Rz Layer\")\n",
    "\n",
    "\n",
    "plt.show()\n",
    "\n",
    "time_span = time.time() - time_start        \n",
    "print('主程序段总共运行了', time_span, '秒')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 更多的量子比特\n",
    "\n",
    "接着我们再看看不断的增加量子比特的数量，会对我们的梯度带来什么影响。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-02T12:24:12.650054Z",
     "start_time": "2021-03-02T12:22:06.472689Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "主程序段总共运行了 144.23979568481445 秒\n"
     ]
    }
   ],
   "source": [
    "# 超参数设置\n",
    "selected_qubit = [2, 4, 6, 8]\n",
    "samples = 300\n",
    "grad_val = []\n",
    "means, variances = [], []\n",
    "\n",
    "\n",
    "# 记录运算时长\n",
    "time_start = time.time()\n",
    "\n",
    "# 不断增加量子比特数量\n",
    "for N in selected_qubit:\n",
    "    grad_info = []\n",
    "    THETA_SIZE = N\n",
    "    for i in range(samples):\n",
    "        class manual_gradient(paddle.nn.Layer):\n",
    "            # 初始化一个长度为 THETA_SIZE 的可学习参数列表\n",
    "            def __init__(self, shape, param_attr=paddle.nn.initializer.Uniform(low=0.0, high=2 * np.pi),dtype='float64'):\n",
    "                super(manual_gradient, self).__init__()\n",
    "\n",
    "                # 转换成 Paddle 中的 Tensor\n",
    "                self.H = paddle.to_tensor(density_op(N))\n",
    "\n",
    "            # 定义损失函数和前向传播机制    \n",
    "            def forward(self):\n",
    "\n",
    "                # 初始化三个 theta 参数列表\n",
    "                theta_np = np.random.uniform(low=0., high= 2 * np.pi, size=(THETA_SIZE))\n",
    "                theta_plus_np = np.copy(theta_np) \n",
    "                theta_minus_np = np.copy(theta_np) \n",
    "\n",
    "                # 修改用以计算解析梯度\n",
    "                theta_plus_np[0] += np.pi/2\n",
    "                theta_minus_np[0] -= np.pi/2\n",
    "\n",
    "                # 转换成 Paddle 中的 Tensor\n",
    "                theta = paddle.to_tensor(theta_np)\n",
    "                theta_plus = paddle.to_tensor(theta_plus_np)\n",
    "                theta_minus = paddle.to_tensor(theta_minus_np)\n",
    "\n",
    "                # 生成随机目标，在 rand_circuit 中随机选取电路门\n",
    "                target = np.random.choice(3, N)      \n",
    "                \n",
    "                U = rand_circuit(theta, target, N)\n",
    "                U_dagger = dagger(U) \n",
    "                U_plus = rand_circuit(theta_plus, target, N)\n",
    "                U_plus_dagger = dagger(U_plus) \n",
    "                U_minus = rand_circuit(theta_minus, target, N)\n",
    "                U_minus_dagger = dagger(U_minus) \n",
    "\n",
    "                \n",
    "                # 计算解析梯度\n",
    "                grad = (paddle.real(matmul(matmul(U_plus_dagger, self.H), U_plus))[0][0]  \n",
    "                        - paddle.real(matmul(matmul(U_minus_dagger, self.H), U_minus))[0][0])/2\n",
    "                \n",
    "                return grad      \n",
    "            \n",
    "           \n",
    "        # 定义主程序段    \n",
    "        def main():\n",
    "            \n",
    "            # 设置QNN的维度\n",
    "            sampling = manual_gradient(shape=[THETA_SIZE])\n",
    "\n",
    "            # 采样获得梯度信息\n",
    "            grad = sampling()\n",
    "\n",
    "            return grad.numpy()\n",
    "        \n",
    "        if __name__ == '__main__':\n",
    "            grad = main()\n",
    "            grad_info.append(grad)\n",
    "            \n",
    "    # 记录采样信息\n",
    "    grad_val.append(grad_info) \n",
    "    means.append(np.mean(grad_info))\n",
    "    variances.append(np.var(grad_info))\n",
    "\n",
    "time_span = time.time() - time_start\n",
    "print('主程序段总共运行了', time_span, '秒')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-02T12:24:30.342035Z",
     "start_time": "2021-03-02T12:24:29.346060Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "我们接着画出这个采样出来的梯度的统计结果：\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 960x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grad = np.array(grad_val)\n",
    "means = np.array(means)\n",
    "variances = np.array(variances)\n",
    "n = np.array(selected_qubit)\n",
    "print(\"我们接着画出这个采样出来的梯度的统计结果：\")\n",
    "fig = plt.figure(figsize=plt.figaspect(0.3))\n",
    "\n",
    "\n",
    "# ============= 第一张图 =============\n",
    "# 统计出随机采样的梯度平均值和量子比特数量的关系\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(n, means, \"o-.\")\n",
    "plt.xlabel(r\"Qubit #\")\n",
    "plt.ylabel(r\"$ \\partial \\theta_{i} \\langle 0|H |0\\rangle$ Mean\")\n",
    "plt.title(\"Mean of {} sampled graident\".format(samples))\n",
    "plt.xlim([1,9])\n",
    "plt.ylim([-0.06, 0.06])\n",
    "plt.grid()\n",
    "\n",
    "# ============= 第二张图 =============\n",
    "# 统计出随机采样的梯度的方差和量子比特数量的关系\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.semilogy(n, variances, \"v\")\n",
    "\n",
    "# 多项式拟合\n",
    "fit = np.polyfit(n, np.log(variances), 1)\n",
    "slope = fit[0] \n",
    "intercept = fit[1] \n",
    "plt.semilogy(n, np.exp(n * slope  + intercept), \"r--\", label=\"Slope {:03.4f}\".format(slope))\n",
    "plt.xlabel(r\"Qubit #\")\n",
    "plt.ylabel(r\"$ \\partial \\theta_{i} \\langle 0|H |0\\rangle$ Variance\")\n",
    "plt.title(\"Variance of {} sampled graident\".format(samples))\n",
    "plt.legend()\n",
    "plt.xlim([1,9])\n",
    "plt.ylim([0.0001, 0.1])\n",
    "plt.grid()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "要注意的是，在理论上，只有当我们选取的网络结构还有损失函数满足一定条件时 (2-design)详见论文 [[1]](https://arxiv.org/abs/1803.11173), 才会出现这种效应。接着我们不妨可视化一下不同量子比特数量对的优化曲面的影响：\n",
    "\n",
    "![BP-fig-qubit_landscape_compare](./figures/BP-fig-qubit_landscape_compare.png \"(a)不固定 z 轴尺度时，采样出的优化曲面分别从左至右对应2、4和6量子比特的情形。（b）同样的优化曲面但是固定 z 轴尺度作为对比。\")\n",
    "<div style=\"text-align:center\">(a)不固定 z 轴尺度时，采样出的优化曲面分别从左至右对应2、4和6量子比特的情形。（b）同样的优化曲面但是固定 z 轴尺度作为对比。</div>\n",
    "         \n",
    "\n",
    "画图时 $\\theta_1$ 和 $\\theta_2$ 是前两个电路参数, 剩余参数全部固定为 $\\pi$。不然我们画不出这个高维度的流形。\n",
    "结果不出所料，陡峭程度随着 $n$ 的增大越来越小了，**注意到 Z 轴尺度的极速减小**。相对于2量子比特的情况，6量子比特的优化曲面已经非常扁平了。\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_______\n",
    "\n",
    "## 参考文献\n",
    "\n",
    "[1] McClean, J. R., Boixo, S., Smelyanskiy, V. N., Babbush, R. & Neven, H. Barren plateaus in quantum neural network training landscapes. [Nat. Commun. 9, 4812 (2018).](https://www.nature.com/articles/s41467-018-07090-4)\n",
    "\n",
    "[2] Cerezo, M., Sone, A., Volkoff, T., Cincio, L. & Coles, P. J. Cost-Function-Dependent Barren Plateaus in Shallow Quantum Neural Networks. [arXiv:2001.00550 (2020).](https://arxiv.org/abs/2001.00550)"
   ]
  }
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     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
